




'''simple docstring'''







import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

lowerCAmelCase_     						=				logging.getLogger(__name__)

lowerCAmelCase_     						=				{'''facebook/bart-base''': BartForConditionalGeneration}
lowerCAmelCase_     						=				{'''facebook/bart-base''': BartTokenizer}







def 						_A      ( ):






   '''simple docstring'''







   A__       =     argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'						)
   parser.add_argument(
       '--validation_file'      ,type=UpperCAmelCase      ,default=UpperCAmelCase      ,help='A csv or a json file containing the validation data.'						)
   parser.add_argument(
       '--max_length'      ,type=UpperCAmelCase      ,default=5      ,help='The maximum total input sequence length after tokenization.'      ,)
   parser.add_argument(
       '--num_beams'      ,type=UpperCAmelCase      ,default=UpperCAmelCase      ,help=(
           'Number of beams to use for evaluation. This argument will be '
           'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
       )      ,)
   parser.add_argument(
       '--model_name_or_path'      ,type=UpperCAmelCase      ,help='Path to pretrained model or model identifier from huggingface.co/models.'      ,required=UpperCAmelCase      ,)
   parser.add_argument(
       '--config_name'      ,type=UpperCAmelCase      ,default=UpperCAmelCase      ,help='Pretrained config name or path if not the same as model_name'      ,)
   parser.add_argument(
       '--device'      ,type=UpperCAmelCase      ,default='cpu'      ,help='Device where the model will be run'      ,)
   parser.add_argument('--output_file_path'      ,type=UpperCAmelCase      ,default=UpperCAmelCase      ,help='Where to store the final ONNX file.'						)

   A__       =     parser.parse_args()

   return args







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase="cpu"						):






   '''simple docstring'''







   A__       =     model_dict[model_name].from_pretrained(UpperCAmelCase						).to(UpperCAmelCase						)
   A__       =     tokenizer_dict[model_name].from_pretrained(UpperCAmelCase						)

   if model_name in ["facebook/bart-base"]:
      A__       =     0
      A__       =     None
      A__       =     0

   return huggingface_model, tokenizer







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   model.eval()

   A__       =     None
   A__       =     torch.jit.script(BARTBeamSearchGenerator(UpperCAmelCase						)						)

   with torch.no_grad():
      A__       =     'My friends are cool but they eat too many carbs.'
      A__       =     tokenizer([ARTICLE_TO_SUMMARIZE]      ,max_length=1024      ,return_tensors='pt'						).to(model.device						)

      A__       =     model.generate(
          inputs['input_ids']      ,attention_mask=inputs['attention_mask']      ,num_beams=UpperCAmelCase      ,max_length=UpperCAmelCase      ,early_stopping=UpperCAmelCase      ,decoder_start_token_id=model.config.decoder_start_token_id      ,)

      torch.onnx.export(
          UpperCAmelCase      ,(
              inputs['input_ids'],
              inputs['attention_mask'],
              num_beams,
              max_length,
              model.config.decoder_start_token_id,
          )      ,UpperCAmelCase      ,opset_version=14      ,input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']      ,output_names=['output_ids']      ,dynamic_axes={
              'input_ids': {0: 'batch', 1: 'seq'},
              'output_ids': {0: 'batch', 1: 'seq_out'},
          }      ,example_outputs=UpperCAmelCase      ,)

      logger.info('Model exported to {}'.format(UpperCAmelCase						)						)

      A__       =     remove_dup_initializers(os.path.abspath(UpperCAmelCase						)						)

      logger.info('Deduplicated and optimized model written to {}'.format(UpperCAmelCase						)						)

      A__       =     onnxruntime.InferenceSession(UpperCAmelCase						)
      A__       =     ort_sess.run(
          UpperCAmelCase      ,{
              'input_ids': inputs['input_ids'].cpu().numpy(),
              'attention_mask': inputs['attention_mask'].cpu().numpy(),
              'num_beams': np.array(UpperCAmelCase						),
              'max_length': np.array(UpperCAmelCase						),
              'decoder_start_token_id': np.array(model.config.decoder_start_token_id						),
          }      ,)

      np.testing.assert_allclose(summary_ids.cpu().numpy()      ,ort_out[0]      ,rtol=1e-3      ,atol=1e-3						)

      logger.info('Model outputs from torch and ONNX Runtime are similar.'						)
      logger.info('Success.'						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     parse_args()
   A__       =     5
   A__       =     4

   # Make one log on every process with the configuration for debugging.
   logging.basicConfig(
       format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'      ,datefmt='%m/%d/%Y %H:%M:%S'      ,level=logging.INFO      ,)

   logger.setLevel(logging.INFO						)
   transformers.utils.logging.set_verbosity_error()

   A__       =     torch.device(args.device						)

   A__  ,			A__       =     load_model_tokenizer(args.model_name_or_path      ,UpperCAmelCase						)

   if model.config.decoder_start_token_id is None:
      raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'						)

   model.to(UpperCAmelCase						)

   if args.max_length:
      A__       =     args.max_length

   if args.num_beams:
      A__       =     args.num_beams

   if args.output_file_path:
      A__       =     args.output_file_path
   else:
      A__       =     'BART.onnx'

   logger.info('Exporting model to ONNX'						)
   export_and_validate_model(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)


if __name__ == "__main__":
    main()








'''simple docstring'''







def 						_A      ( UpperCAmelCase = 1      ,UpperCAmelCase = 1000						):






   '''simple docstring'''







   A__       =     1
   A__       =     0

   for divide_by_number in range(UpperCAmelCase      ,digit + 1						):
      A__       =     []
      A__       =     numerator
      for _ in range(1      ,digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(UpperCAmelCase						):
               A__       =     len(UpperCAmelCase						)
               A__       =     divide_by_number
         else:
            has_been_divided.append(UpperCAmelCase						)
            A__       =     now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


